Principal AI Engineer
Tasks
- Architect graph based agent workflows
- Build RESTful and event driven APIs
- Build hybrid intelligence patterns
- Build reusable agent node libraries
- Design autonomous multi-agent workflows
- Develop production grade FastAPI applications
- Develop response transformation pipelines
- Ensure data governance and responsible AI
- Implement access control and error handling
- Implement conversation state persistence
- Implement evaluation frameworks for agents
- Implement real time agent streaming
- Implement tool calling protocols
- Integrate LDAP and SSO
- Integrate PostgreSQL and Redis
- Integrate cloud based LLM providers
- Integrate observability and tracing
- Manage prompts and templates
- Mentor engineers on async Python and LLMOps
- Optimize token usage and LLM costs
- Participate in architecture reviews
- Translate requirements into agent workflows
Perks/Benefits
Skills/Tech-stack
AWS | AWS Bedrock | Access Control | Anthropic Claude | Asyncio | Autogen | Azure | Azure OpenAI | CI/CD | CrewAI | Docker | End to End | End-to-End Testing | Error Handling | Event Driven | Event-driven architecture | FastAPI | GPT-4 | Git | Google Cloud | Integration Testing | LDAP | LLMOps | Langchain | Langfuse | Langgraph | Langsmith | Model Context Protocol | Observability | OpenAI GPT | OpenAI GPT-4 | PostgreSQL | Prompt engineering | Prompt templating | Python | REST API | Redis | Responsible AI | SSE | SSO | Unit Testing | WebSocket
Education
Bachelor of Engineering | Bachelor of Science | Master of Science
Roles
AI | AI Engineer | Engineer | Learning Engineer | Machine Learning Engineer | Principal | Principal AI Engineer
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